This paper proposes an alternative to solve the distribution network reconfiguration (DNR) problem aiming real power losses minimization. For being a problem that has a complexity for its solution, approximate techniques are adequate for solving it. Here, the proposition is a technique based on the firefly metaheuristic, named selective firefly algorithm, where the positioning of these insects are compressed in a selective range of values. The algorithm is applied on DNR, and all its implementation and adequacy to the problem studied are presented. To define the search space, the methodology presented initially considers a set of candidate switches for opening based on the studied systems mesh analysis. To reduce these possibilities, a refinement through a load flow analysis criterion (LFAC) is proposed. This LFAC considers the real power losses on each branch for a configuration with all switches closed, then, selecting possible switches to elimination from the set previously established. To demonstrate the behavior and the viability of the LFAC, it was initially applied on a 5 buses and 7 branches system. Also, to avoid getting stuck on results that may be considered not good, a disturbance resetting the population is set to occur every time a counter reaches a pre-defined number of times that the best solution does not change. Results found for simulations with 33, 70, and 84 buses are presented and comparisons with selective particle swarm optimization (SPSO) and selective bat algorithm (SBAT) are made.

Reactive power compensation with Capacitor Banks (CBs) is one of the most successful approaches used in distribution systems, mainly due to their versatility, long-term acceptance in the power industry, and reduced costs. Most allocation methods, however, lack specific strategies to handle the limited discrete nature of CBs sizes seeking to improve the overall optimization and computational performance. We present an algorithm for the Optimal Placement of Capacitor Banks (OPCB) in distribution systems by means of a hybrid Flower Pollination Algorithm (FPA)–Exhaustive Search (ES) approach. The pollination process itself determines the sets of buses for placement, while CBs sizes and the final fitness values of each pollen are selected after a full-search is conducted in the sizing space. As the sizing phase works on the limited search space of predetermined discrete bank values, the computational effort to find the optimum CB capacity is greatly reduced. Tests were performed on distribution systems of 10, 34, and 85 buses with respect to the objective function, final losses, and voltage profile. The algorithm offers an excellent compromise between solution quality and computational effort, when compared to similar approaches.